1. Field of the Invention
This invention is directed to a method and apparatus for establishing the position of one or more constituent elements of a phantom in an image quality test, such as the NEMA image quality test.
2. Description of the Prior Art
In the medical imaging field, several nuclear medicine emission imaging schemes are known. For example PET (Positron Emission Tomography) is a method for imaging a subject in 3D using an ingested radio-active substance which is processed in the body, typically resulting in an image indicating one or more biological functions. FDG, for instance, is a glucose analogue which is used as the radiopharmaceutical tracer in PET imaging to show a map of glucose metabolism.
To assess how accurately a medical imaging apparatus, such as a Positron Emission Tomography (PET) scanner, is performing, a series of tests are performed. These tests will be run when a new scanner is installed, and may be repeated at regular intervals to ensure that the performance of the scanner is still optimal.
To check medical imaging scanners, tests using scans on standard control objects are typically performed. One set of tests on PET scanners that are run are known as the NEMA Part 7 Image Quality tests (NEMA Standards Publication NU 2-2007 (2007) Performance Measurements of Positron Emission Tomographs, which are used to determine how well the scanner resolves small hot and cold lesions, and how well the scatter correction software algorithm is performing.
As described in the aforementioned NEMA Standards Publication, when running these tests, several compartments in an image phantom are filled with liquid containing different concentrations of 18F solution. The relevant statistics are computed by placing regions of interest (ROIs) on the image of the phantom as specified in the NEMA Standards Publication. To date, all known systems for analysing the results require at least some manual steps for the positioning of the ROIs.
Specific systems for aiding performance of tests such as the NEMA IQ test have been previously considered. As an alternative to these specific applications, some end-user sites may use their own custom MATLAB/IDL scripts.
The common feature of all of previously considered systems is that a level of manual intervention is required from the user. For instance, one of the previous applications will optimise the 3D position of each of the hot/cold sphere ROIs in a NEMA test based on an initial seed pixel selected by the user (one click for each ROI). The background and lung insert ROIs are then placed automatically and the appropriate statistics calculated.
Another previous application, requires even more manual input, with every hot/cold sphere ROI and every background ROI requiring explicit placement by the user.
Another requires extensive manual intervention. Specifically, manual selection of the axial slice to be used for analysis, along with manual placement of all 6 hot/cold sphere ROIs, 12 background ROIs and 1 lung insert ROI.